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A Fuzzy-SOM Method for Fraud Detection in Power Distribution Networks with High Penetration of Roof-Top Grid-Connected PV

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A Fuzzy-SOM Method for Fraud Detection in Power Distribution Networks with High Penetration of Roof-Top Grid-Connected PV energies Article A Fuzzy-SOM Method for Fraud Detection in Power Distribution Networks with High Penetration of Roof-Top Grid-Connected PV Alireza Vahabzadeh 1 , Alibakhsh Kasaeian 1,*, Hasan Monsef 2 and Alireza Aslani 1 1 Faculty of New Sciences and Technologies, University of Tehran, Tehran 1439957131, Iran; [email protected] (A.V.); [email protected] (A.A.) 2 School of Electrical and Computer Engineering, University of Tehran, Tehran 1417414418, Iran; [email protected] * Correspondence: [email protected]; Tel.: +98-912-194-7510; Fax: +98-21-884-97324 Received: 2 February 2020; Accepted: 4 March 2020; Published: 10 March 2020 Abstract: This study proposes a fuzzy self-organized neural networks (SOM) model for detecting fraud by domestic customers, the major cause of non-technical losses in power distribution networks. Using a bottom-up approach, normal behavior patterns of household loads with and without photovoltaic (PV) sources are determined as normal behavior. Customers suspected of energy theft are distinguished by calculating the anomaly index of each subscriber. The bottom-up method used is validated using measurement data of a real network. The performance of the algorithm in detecting fraud in old electromagnetic meters is evaluated and verified. Types of energy theft methods are introduced in smart meters. The proposed algorithm is tested and evaluated to detect fraud in smart meters also. Keywords: fraud-detection; non-technical loss; power distribution; load profile modeling; data mining; fuzzy-SOM 1. Introduction Power grids fall into three main sectors of production, transmission and distribution. A large portion of the power grid losses belongs to distribution networks due to their expansiveness, speed of development and inappropriate operation. In 2018, 10.79% of the electricity delivered to Iran’s national grid was lost in distribution. That is equivalent to a stunning amount of 32 billion kWh [1]. Generally, electrical energy losses are the portion of electricity that is injected into the transmission and distribution grid but is not paid for by end-users. In other words, part of the electricity injected into the transmission and distribution grid, which does not generate any revenue for the electricity providers, is called loss of electricity. Electricity losses consist of two main components, namely “technical losses” and “non-technical losses”. Technical losses occur due to the current passing through the inherent characteristic of electrical resistance of the conductors of the grid. These types of losses are found in transmission and distribution lines, transformers and metering systems. Non-technical losses originate from off-system activities and occur in various forms, including electricity theft, non-payment by subscribers, and errors in recording and calculating energy costs. An important issue facing the problem of losses in electricity distribution networks is the serious challenge of unclear share of technical and non-technical losses in the networks. Calculated technical losses are not reliable because of insufficient information, poor quality of equipment, poor installation quality and low operational quality. Introduction of small-scale generators into the grid, and especially small domestic solar units, as well as wind mini-generators, micro-hydro generators, etc. has increased the complexity Energies 2020, 13, 1287; doi:10.3390/en13051287 www.mdpi.com/journal/energies Energies 2020, 13, 1287 2 of 24 of assessing electrical energy losses, especially in the non-technical sector. The conventional rule of detecting non-technical losses in power distribution companies includes field visiting and testing customer meters. The aforementioned methods result in less detection of those subscribers who disrupt their meters at certain times but for the whole metering period. The non-technical losses imposed on the power grid thus account for a significant share of total non-technical losses. In addition, the high number of customers and the need to test all network meters make these methods very costly. In practice, in many cases the high cost of testing equipment, the cost of manpower and the cost of transporting swallows a large portion of the revenue from non-technical loss recovery. Numerous studies have so far provided useful solutions to detect such fraud. A method for detecting fraud by high-voltage customers is presented in [2,3]. The methods presented in these studies are based on artificial intelligence and data mining and have achieved successful results. Another interesting research work is that of Monedero et al. [4]. The proposed method based on artificial neural networks was used in the Spanish Seville power grid, and the remarkable result was a 50% improvement in the detection of non-technical casualties compared to previous methods. In general, methods for detecting non-technical losses and unauthorized use of the power grid can be divided into three categories: theoretical methods, hardware methods and data analysis methods [5]. Among these, data-analysis-based methods have the highest share, which in practice are preferred over other methods because of the high cost of meter testing or installation of consumption monitoring hardware. Numerous proposed and tested methods have had significant results in monitoring and reducing technical losses, but technological changes such as smart grids techniques and technologies [6], digital meter technology, and the development of domestic energy generation using on-grid rooftop photovoltaic (PV) panels have created complexities in non-technical loss-detection models. Various articles have focused on the detection of non-technical losses and unauthorized use of the network in smart networks [6–9], but the issue of non-technical losses in networks with large numbers of grid-connected small scale generations has been considered in few research works. The effect of such resources on non-technical losses of distribution grids, has received less attention in the literature. Expanding the use of almost no-cost solar/mini-wind/micro-hydro power (apart from initial investment costs) will generally encourage a minimal use of the costly network energy, but the problem arises when there are similarities between the behavior of subscribers suspected of manipulating meters and subscribers using these small resources. This similarity of consumption behavior makes it difficult to distinguish these two types of subscribers using the methods presented in previous research. This study focuses on rooftop grid-connected solar resources, where the model may be extended to mini-wind and micro-hydro generation using the relevant resource models. There are also some other small-scale resource technologies such as small parabolic solar collectors which are mostly used for heating and cooking, hence are not included in the study. The basic goals of this study can be summarized as follows: Accelerating detection and control of non-technical losses of distribution networks. • Controlling the cost of detecting non-technical losses in power distribution companies. • Providing an efficient model for consumption management. • Modeling the effect of renewable resource development on customers’ load behavior. • Modeling the non-technical losses of distribution networks in this study is fully consistent with a variety of conventional energy theft methods. The comprehensive model presented is applicable to a wide range of distribution networks, both traditional and modern smart distribution networks, which has received little research attention. Customers’ energy consumption is measured using modern smart meters in the Iranian power distribution network, but there are still significant numbers of electromagnetic or old digital meters in the network. Due to the differing data resolution available in traditional meters and smart meters, the detection method of non-technical losses of these two types of subscribers is different in some stages. The method presented in this study is based on data-mining models. Since some subscribers who have Energies 2020, 13, 1287 3 of 24 successfully passed the meter test process may also have attempted energy theft, the consumption data of the verified meters alone cannot provide a precise pattern of customer consumption behavior. We use a bottom-up approach to determine the electrical energy consumption pattern. Electrical load profiles are generated for customers with and without rooftop photovoltaic resources. The proposed bottom-up model simulates the random behavior of the customers and, thus, the load profile may be different from one customer to another. The effect of rooftop PV on load profile is then. This study considers photovoltaic systems equipped with maximum power point tracking (MPPT). A method based on self-organized neural networks (SOM), is proposed to detect suspected subscribers to fraud and consumption anomalies. The model is then evaluated in a real sample network. 2. Modeling Domestic Load Profile We need to know the behavior of loads, and in particular, domestic customers in this study. Some studies of power distribution networks such as master planning, loss reduction studies and protection coordination studies use cumulative models of load behavior. A common method of analyzing load behavior is to measure the simultaneous power consumption of a number of customers of a particular category (e.g., domestic, commercial, industrial, etc.) and extend it to other customers of the same category using coincidence factors [10]. The
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